The Butterfly Effect in Primary Visual Cortex

TL;DR

This paper introduces the continuous-coupled neural network (CCNN), inspired by the primary visual cortex, which captures complex neuronal dynamics and outperforms existing models in image segmentation tasks.

Contribution

The paper proposes the CCNN model that incorporates stochastic neuronal excitation, capturing chaotic behaviors and improving image processing performance over prior PCNN models.

Findings

CCNN exhibits periodic behavior under DC stimulus.

CCNN exhibits chaotic behavior under AC stimulus.

CCNN outperforms state-of-the-art models in image segmentation.

Abstract

Exploring and establishing artificial neural networks with electrophysiological characteristics and high computational efficiency is a popular topic in the field of computer vision. Inspired by the working mechanism of primary visual cortex, pulse-coupled neural network (PCNN) can exhibit the characteristics of synchronous oscillation, refractory period, and exponential decay. However, electrophysiological evidence shows that the neurons exhibit highly complex non-linear dynamics when stimulated by external periodic signals. This chaos phenomenon, also known as the " butterfly effect", cannot be explained by all PCNN models. In this work, we analyze the main obstacle preventing PCNN models from imitating real primary visual cortex. We consider neuronal excitation as a stochastic process. We then propose a novel neural network, called continuous-coupled neural network (CCNN). Theoretical analysis indicates that the dynamic behavior of CCNN is distinct from PCNN. Numerical results show that the CCNN model exhibits periodic behavior under DC stimulus, and exhibits chaotic behavior under AC stimulus, which is consistent with the results of real neurons. Furthermore, the image and video processing mechanisms of the CCNN model are analyzed. Experimental results on image segmentation indicate that the CCNN model has better performance than the state-of-the-art of visual cortex neural network models.